This work presents a novel and rapid approach to predict fat content in butter products based on nuclear magnetic resonance longitudinal (T1) relaxation measurements and multi-block chemometric methods. The potential of using simultaneously liquid (T1L) and solid phase (T1S) signals of fifty samples of margarine, butter and concentrated fat by Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Selective Covariance Selection (SO-CovSel) methods was investigated. The two signals (T1L and T1S) were also used separately with PLS and CovSel regressions. The models were compared in term of prediction errors (RMSEP) and repeatability error (σrep). The results obtained from liquid phase (RMSEP ≈ 1.33% and σrep≈ 0.73%) are better than those obtained with solid phase (RMSEP ≈ 5.27% and σrep≈ 0.69%). Multiblock methodologies present better performance (RMSEP ≈ 1.00% and σrep≈ 0.47%) and illustrate their power in the quantitative analysis of butter products. Moreover, SO-Covsel results allow for proposing a measurement protocol based on a limited number of NMR acquisitions, which opens a new way to quantify fat content in butter products with reduced analysis times.
Simultaneous Variable and Covariance Selection With the Multivariate Spike-and-Slab LASSO
